Related Experiment Video
Updated: Jul 28, 2025
Evaluation of Coronary Flow Reserve After Myocardial Ischemia Reperfusion in Rats
Published on: June 28, 2019
Predictive value of DEEPVESSEL-fractional flow reserve and quantitative plaque analysis based on coronary CT
1Department of Interventional Radiology, The First Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou University of Chinese Medicine, Guangzhou, China.
Insights
Deep vessel fractional flow reserve (DVFFR) and plaque analysis from coronary computed tomographic angiography (CCTA) accurately predict major adverse cardiac events (MACE). Combining these non-invasive methods offers valuable prognostic information for MACE risk stratification.
Area of Science:
- Cardiology
- Medical Imaging
- Computational Biology
Background:
- Non-invasive assessment of coronary artery disease severity is crucial for patient management.
- Fractional flow reserve (FFR) is a key physiological parameter, but invasive measurement carries risks.
- Coronary computed tomographic angiography (CCTA) provides anatomical detail and is increasingly used for plaque characterization.
Purpose of the Study:
- To evaluate the predictive capability of DEEPVESSEL-fractional flow reserve (DVFFR) combined with quantitative plaque analysis from CCTA for major adverse cardiac events (MACE).
- To assess the diagnostic performance of DVFFR in identifying hemodynamically significant coronary lesions.
- To determine the incremental value of combining non-invasive DVFFR and plaque burden metrics for MACE prediction.
Main Methods:
- Retrospective analysis of 69 vessels from 58 patients undergoing coronary angiography (CAG) with DVFFR.
- DVFFR derived from CCTA images, with a threshold of ≤0.80 indicating hemodynamic significance.
- Quantitative CCTA plaque analysis included total plaque burden, calcified and non-calcified plaque, low-attenuation plaque, minimum lumen area, stenosis grade, and lesion length.
- Statistical analysis involved logistic regression, correlation, and ROC analyses to assess MACE prediction.
Main Results:
- DVFFR demonstrated high correlation (R=0.728) and agreement with invasive FFR, with excellent diagnostic performance (AUC=0.984) for identifying significant lesions.
- Multivariate analysis identified DVFFR (≤0.8), stenosis grade, total plaque burden, non-calcified plaque burden, and lesion length as independent predictors of MACE.
- The combination of these factors provided the most robust prediction for MACE (AUC=0.888).
Conclusions:
- DVFFR is a reliable non-invasive method highly correlated with invasive FFR, offering satisfactory diagnostic accuracy.
- The integration of DVFFR with quantitative plaque analysis from CCTA provides valuable predictive insights into major adverse cardiac events.
- This combined approach holds promise for improved, non-invasive risk stratification in patients with coronary artery disease.
Aim:
To investigate the predictive value of the combination of DEEPVESSEL-fractional flow reserve (DVFFR) and quantitative plaque analysis using coronary computed tomographic angiography (CCTA) for major adverse cardiac events (MACE).
Method:
In this retrospective study, data from 69 vessels from 58 consecutive patients were collected. These patients who underwent coronary angiography (CAG) with DVFFR were divided into MACE-positive and MACE-negative groups. DVFFR measurements were obtained from CCTA images acquired before CAG, and an FFR or DVFFR value ≤ 0.80 was considered haemodynamically significant. CCTA images were analysed quantitatively using automated software to obtain the following indices: total plaque volume (TPV) and burden (TPB), calcified plaque volume (CPV) and burden (CPB), non-calcified plaque volume (NCPV) and burden (NCPB), low-attenuation plaque (LAP), minimum lumen area (MLA), stenosis grade (SG) and lesion length (LL). Univariate and multivariate logistic regression, correlation, and receiver operating characteristic (ROC) analyses were used for statistical analysis.
Results:
DVFFR was highly correlated with invasive FFR (R=0.728), and the Bland-Altman plot showed good agreement between DVFFR and FFR (95% CI: -0.109-0.087) on a per-vessel level. DVFFR showed a high diagnostic performance in identifying abnormal haemodynamic vessels, with an area under the ROC curve (AUC) of 0.984. In multivariate analysis, the following biomarkers were predictors of MACE: DVFFR ≤ 0.8, SG, TPB, NCPB, and LL values. The combination of the above independent risk factors yielded the most valuable prediction for MACE (AUC:0.888).
Conclusions:
DVFFR was highly correlated with FFR with satisfactory diagnostic accuracy. DVFFR, together with plaque analysis indices, yielded valuable predictions for MACE.
More Related Videos
05:07Author Spotlight: Improved Localization and Monitoring of Coronary Flow Reserve Using Modified PLAX View in Mice
Published on: August 25, 2023
13:07Optical Coherence Tomography Based Biomechanical Fluid-Structure Interaction Analysis of Coronary Atherosclerosis Progression
Published on: January 15, 2022
Related Concept Videos
01:25Imaging Studies for Cardiovascular System VI: Calcium -Scoring CT
01:28Imaging Studies for Cardiovascular System V: CT
01:30Acute Coronary Syndrome III: Diagnostic Studies